The presence of multiple coexisting chronic diseases in individuals and the expected rise in chronic diseases over the coming years are increasingly being recognized as major public health and health care challenges of modern societies (Marengoni et al., 2011; WHO, 2009; Vogeli et al., 2007; Glynn et al., 2011; Smith and O’Dowd, 2007; Barnett et al., 2012). Individuals with multiple conditions are presumed to have greater health needs, more risk of complications, and more difficulty to manage treatment regimens. At present, the main health care model is disease-focused rather than person-focused and, therefore, involvement of several different health care providers in managing multiple disorders is inevitable and often results in competing treatments, sub-optimal coordination and communication between care providers, and/or unnecessary replication of diagnostic tests or treatments (Vogeli et al., 2007; Clarfield et al., 2001; Greß et al., 2009). As a consequence, the common belief is that persons with multiple diseases have high rates of health care utilization and this is confirmed by some international studies (Glynn et al., 2011; Starfield, 2006; Fortin et al., 2007; Laux et al., 2008; Salisbury et al., 2011; van den Bussche et al., 2011; Lehnert et al., 2011).
In our article we use SHARE dataset of Wave 5 (covering year 2013), including data on 14 European countries and Israel. We model the presence of multiple coexisting chronic diseases as a two-mode network analysis problem. This has special scientific relevance as, to our knowledge, network analysis has not been used so far to study this problem, and, also, very seldom before in the analysis using SHARE data. In our case, therefore, the diseases are vertices/nodes and individuals having them the edges. Controlling for the frequency of network relationships, we calculate different network parameters (e.g. centrality parameters) and use them in econometric modelling. To appropriately model the presence of multiple chronic diseases we also use tools from multivariate analysis (mainly factor and principal components analysis and cluster analysis) and blockmodelling.
Finally, to verify the effects of multiple diseases on the rates of health care utilization we construct four different health care utilization variables: frequency of medical visits, number of taken medications, frequency of hospitalizations and probability of hospitalization. Main research questions of the analysis are: 1) What are the most frequent combinations of chronic diseases? 2) Which are the most common groupings of diseases which can be characterized from the data for the older people in Europe? 3) What are the effects of multiple coexisting chronic diseases on health care utilization of the older people when controlling for different groupings of diseases? 4) Are there different effects on health care utilization for different groupings of diseases? We model the effects of different combinations of most commonly connected diseases on the health care utilization using econometric models from causal inference (controlling for endogeneity). Finally, in conclusions, we provide reflection of the findings for future work and policy relevance of the study.